data("ny_elec")
head(ny_elec)
#> date_time y
#> 1 2015-07-01 05:00:00 14444
#> 2 2015-07-01 06:00:00 13809
#> 3 2015-07-01 07:00:00 13435
#> 4 2015-07-01 08:00:00 13144
#> 5 2015-07-01 09:00:00 13147
#> 6 2015-07-01 10:00:00 13771
class(ny_elec)
#> [1] "tbl_ts" "tbl_df" "tbl" "data.frame"
library(TSstudio)
ts_plot(ny_elec,
title = "Net Generation of Electricity for the New York Region",
Ytitle = "Megawatthours",
Xtitle = "Source: US Energy Information Administration (Jan 2020)",
slider = TRUE)
md1 <- trainLM(input = ny_elec,
y = "y",
seasonal = c("month"),
trend = list(linear = TRUE))
plot_res(md1)
md2 <- trainLM(input = ny_elec,
y = "y",
seasonal = c("month", "wday"),
trend = list(linear = TRUE))
plot_res(md2)
md3 <- trainLM(input = ny_elec,
y = "y",
seasonal = c("month", "wday", "hour"),
trend = list(linear = TRUE))
plot_res(md3)
md4 <- trainLM(input = ny_elec,
y = "y",
seasonal = c("month", "wday", "hour"),
trend = list(linear = TRUE),
lags = c(1:24))
plot_res(md4)
md5 <- trainLM(input = ny_elec,
y = "y",
seasonal = c("month", "wday", "hour"),
trend = list(linear = TRUE),
lags = c(1:24, 48, 72))
plot_res(md5)
summary(md5)
#> Length Class Mode
#> model 13 lm list
#> fitted 2 data.frame list
#> residuals 2 tbl_ts list
#> parameters 13 -none- list
#> series 32 tbl_ts list